Predictive Analytics II: Text, Web, and Social Media Analytics - Text Analytics and Text Mining Overview
4 important questions on Predictive Analytics II: Text, Web, and Social Media Analytics - Text Analytics and Text Mining Overview
What is Text Analytics? How does it differ from text mining?
information retrieval, information extraction, data mining and web mining
Text Mining: focuses on discovering new and useful knowledge from the textual data sources.
What is text mining? How does it differ from data mining?
Text Mining is the same as data mining, with the same purpose and uses the same processes, but with unstructured text sources as input instead of structured data as with data mining.
It can be thought of a 2-step process: 1) impose structure on the text-based data sources, 2) extract relevant information and knowledge from this structured text-based data using data mining techniques and tools
Why is the popularity of text mining as an analytics tool increasing?
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What are som of the most popular application areas of text mining?
Topic tracking: predict other documents of interest to the user
Summarization
Categorization: Identify Main themes and place the doc into a predefiend set of categories
Clustering: group similar docs without having a predefined set of categories
Concept linking: connect docs by identifying shared concepts and help users find information they would not have found (perhaps)
Question answering: find the best answers to a given question knowledge-driven pattern matching
The question on the page originate from the summary of the following study material:
- A unique study and practice tool
- Never study anything twice again
- Get the grades you hope for
- 100% sure, 100% understanding
